1 citations · 1 across the 4 of their papers we have counts for
4 papers
TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels
Siow Meng Low, Ze Gong, Akshat Kumar
Ensuring safe behavior in reinforcement learning (RL) is challenging when safety constraints are implicit and cannot be densely measured. In many settings, supervision is limited t…
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints
Siow Meng Low, Akshat Kumar
In safe Reinforcement Learning (RL), safety cost is typically defined as a function dependent on the immediate state and actions. In practice, safety constraints can often be non-M…
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects
Siow Meng Low, Akshat Kumar, Scott Sanner
In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In the real world, often the state representation used may lack…
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs
Siow Meng Low, Akshat Kumar, Scott Sanner
Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and…